| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1018 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 90.18% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1018 | | totalAiIsms | 2 | | found | | | highlights | | |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 87 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 87 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 93 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 13 | | totalWords | 1019 | | ratio | 0.013 | | matches | | 0 | "missing, presumed." | | 1 | "I should have followed." | | 2 | "whatever took him is still there." | | 3 | "Don't." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 984 | | uniqueNames | 19 | | maxNameDensity | 0.81 | | worstName | "Quinn" | | maxWindowNameDensity | 1 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | High | 1 | | Street | 1 | | Harlow | 1 | | Quinn | 8 | | Tomás | 1 | | Herrera | 3 | | Spaniard | 1 | | Raven | 1 | | Nest | 1 | | Service | 1 | | Bengali | 1 | | Underground | 1 | | Road | 1 | | Tube | 1 | | Morris | 1 | | Rain | 3 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Spaniard" | | 5 | "Raven" | | 6 | "Morris" | | 7 | "Rain" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Service" | | 4 | "Bengali" | | 5 | "Road" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 54 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a man trying to warn her" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1019 | | matches | (empty) | |
| 59.14% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 93 | | matches | | 0 | "liked that choice" | | 1 | "told, that she" | | 2 | "told, that she" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 26.82 | | std | 23.23 | | cv | 0.866 | | sampleLengths | | 0 | 57 | | 1 | 15 | | 2 | 2 | | 3 | 85 | | 4 | 34 | | 5 | 6 | | 6 | 43 | | 7 | 2 | | 8 | 49 | | 9 | 36 | | 10 | 6 | | 11 | 67 | | 12 | 12 | | 13 | 53 | | 14 | 5 | | 15 | 15 | | 16 | 20 | | 17 | 4 | | 18 | 18 | | 19 | 4 | | 20 | 62 | | 21 | 21 | | 22 | 53 | | 23 | 21 | | 24 | 2 | | 25 | 5 | | 26 | 40 | | 27 | 62 | | 28 | 1 | | 29 | 61 | | 30 | 29 | | 31 | 12 | | 32 | 13 | | 33 | 56 | | 34 | 9 | | 35 | 1 | | 36 | 25 | | 37 | 13 |
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| 89.13% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 87 | | matches | | 0 | "been decommissioned" | | 1 | "been stripped" | | 2 | "being told" | | 3 | "being told" |
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| 47.62% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 175 | | matches | | 0 | "was shouting" | | 1 | "was crying" | | 2 | "were breathing" | | 3 | "was making" |
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| 81.41% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 93 | | ratio | 0.022 | | matches | | 0 | "The doorway exhaled a smell that had no business rising from any Tube station she'd ever known — wet iron and something sweet underneath, like crushed lilies left too long in a vase." | | 1 | "The moon above the clouds was full; she'd noticed it driving over." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 990 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 37 | | adverbRatio | 0.03737373737373737 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0030303030303030303 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 93 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 93 | | mean | 10.96 | | std | 9.13 | | cv | 0.833 | | sampleLengths | | 0 | 27 | | 1 | 30 | | 2 | 15 | | 3 | 2 | | 4 | 2 | | 5 | 4 | | 6 | 34 | | 7 | 18 | | 8 | 14 | | 9 | 13 | | 10 | 4 | | 11 | 3 | | 12 | 27 | | 13 | 6 | | 14 | 6 | | 15 | 26 | | 16 | 11 | | 17 | 2 | | 18 | 3 | | 19 | 2 | | 20 | 31 | | 21 | 13 | | 22 | 9 | | 23 | 5 | | 24 | 22 | | 25 | 6 | | 26 | 8 | | 27 | 4 | | 28 | 19 | | 29 | 4 | | 30 | 32 | | 31 | 2 | | 32 | 10 | | 33 | 2 | | 34 | 4 | | 35 | 2 | | 36 | 33 | | 37 | 12 | | 38 | 5 | | 39 | 15 | | 40 | 1 | | 41 | 8 | | 42 | 11 | | 43 | 4 | | 44 | 6 | | 45 | 12 | | 46 | 4 | | 47 | 13 | | 48 | 5 | | 49 | 30 |
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| 64.86% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.43478260869565216 | | totalSentences | 92 | | uniqueOpeners | 40 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 76 | | matches | | 0 | "Of course he didn't." | | 1 | "Then the parcel had disappeared" | | 2 | "Then he cut left, down" | | 3 | "Somewhere above, a woman was" | | 4 | "Instead, he crouched, yanked a" | | 5 | "Somewhere inside the station, a" |
| | ratio | 0.079 | |
| 72.63% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 76 | | matches | | 0 | "She'd followed him three streets" | | 1 | "She cleared the wall." | | 2 | "Her knee complained." | | 3 | "Her voice cracked against wet" | | 4 | "He threw a glance over" | | 5 | "She expected him to climb." | | 6 | "She reached the fence five" | | 7 | "She came up on the" | | 8 | "Her hand went to the" | | 9 | "She thumbed the button anyway." | | 10 | "She stared at it." | | 11 | "He had not come back." | | 12 | "It said *I should have" | | 13 | "She could go back." | | 14 | "Her chief would sign it" | | 15 | "Her pension liked that choice." | | 16 | "She looked at her watch." | | 17 | "Her mouth tightened." | | 18 | "She drew the baton, snapped" | | 19 | "She counted seventy-two steps before" |
| | ratio | 0.368 | |
| 78.42% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 58 | | totalSentences | 76 | | matches | | 0 | "Rain came down in sheets" | | 1 | "Detective Harlow Quinn ran through" | | 2 | "The Spaniard moved like someone" | | 3 | "Quinn had watched him leave" | | 4 | "She'd followed him three streets" | | 5 | "She cleared the wall." | | 6 | "Her knee complained." | | 7 | "Her voice cracked against wet" | | 8 | "He threw a glance over" | | 9 | "The alley narrowed." | | 10 | "London's ordinary noise, wrapped around" | | 11 | "Herrera hit a chain-link fence" | | 12 | "She expected him to climb." | | 13 | "She reached the fence five" | | 14 | "Rain ran down her collar" | | 15 | "Concrete scraped her shoulder." | | 16 | "She came up on the" | | 17 | "The station had been decommissioned" | | 18 | "The doorway exhaled a smell" | | 19 | "Her hand went to the" |
| | ratio | 0.763 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 76 | | matches | (empty) | | ratio | 0 | |
| 90.59% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 3 | | matches | | 0 | "The Spaniard moved like someone who'd spent years running from things worse than her, shoulders tucked, weight forward, a medical bag slung across his chest tha…" | | 1 | "The doorway exhaled a smell that had no business rising from any Tube station she'd ever known — wet iron and something sweet underneath, like crushed lilies le…" | | 2 | "Call it in from the high street, get a uniformed unit, come back at dawn with warrants and torches and people who carried more than a cold radio and a collapsib…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |